Column

Current and predicted vagrancy conditions

Vagrancy conditions in spring 2022 have been moderate, and are expected to decrease over the next 14 days. Vagrancy conditions are determined here by the factors that influence a bird’s ability to use the Earth’s geomagnetic field to navigate during migration.

Species Richness Trends

The chance to see a rare bird is also determined by seasonality. Here, the relative number of species for this week is indicated by color scale, and +, - and = signs indicate where richness is increasing, decreasing, or staying about the same.

{r, fig.width=10, fig.height=7,fig.align='center',fig.cap="Vagrancy varies year-to-year for each species. Here is the trend for one random species (updated daily)."} # model_in <- readRDS("~/Documents/Coding/R/Pop_Disp_Vagrancy/model_fits/M3_spring_2022_05_30/M3_spring_2022_05_30.rds") # model_df <- readRDS("~/Documents/Coding/R/Pop_Disp_Vagrancy/model_fits/M1_2_3_data_spring_2022_05_30.rds") # # model_params <- as.data.frame(MCMCchains(model_in,params=c("alpha","theta","psi","beta1", # "beta2","mu_beta1","mu_beta2", # "mu_psi","phi"))) # spec_names <- unique(model_df$spec_name) # # Pick species # # spec_num <- sample(1:length(spec_names),1) # # spec_data <- filter(model_df,spec_name == spec_names[spec_num]) # year_pred_rec <- c() # for (n in 1:nrow(spec_data)){ # #Reference # #lp = exp(alpha[ii]+theta*eff+ psi[ii].*xx + beta1[ii].*xx1 + beta2[ii].*xx2); # #yy ~ neg_binomial_2(lp,phi); # # year <- spec_data$Year[n] # eff <- spec_data$total_count_stand[n] # xx <- spec_data$xx[n] # xx1 <- spec_data$xx1[n] # xx2 <- spec_data$xx2[n] # sz <- as.numeric(unlist(model_params["phi"])) # lp <- as.numeric(unlist(model_params[paste("alpha[",spec_num,"]",sep="")])) + # as.numeric(unlist(model_params[paste("theta",sep="")])) * eff + # as.numeric(unlist(model_params[paste("psi[",spec_num,"]",sep="")])) * xx + # as.numeric(unlist(model_params[paste("beta1[",spec_num,"]",sep="")])) * xx1 + # as.numeric(unlist(model_params[paste("beta2[",spec_num,"]",sep="")])) * xx2 # pois_draw <- mean(qnbinom(p = .5,size = sz,mu = exp(lp))) # lower1 <- mean(qnbinom(p = .05,size = sz,mu = exp(lp))) # upper1 <- mean(qnbinom(p = .95,size = sz,mu = exp(lp))) # # # # year_pred <- c(year,pois_draw,lower1,upper1) # # year_pred_rec <- rbind(year_pred_rec,year_pred) # } # year_pred_rec <- as.data.frame(year_pred_rec) # plot(year_pred_rec$V1,year_pred_rec$V2,cex=0.1,col="black",pch=19,ylim=c(0,(max(year_pred_rec$V2)*1.25)), # xlab="Year",ylab="Predicted Vagrancy",main=spec_names[spec_num]) # # points(year_pred_rec$V1,year_pred_rec$V2,type="l",lwd=4,col="dodgerblue4",xlab="Year",ylab="# Vagrants",main=spec_names[spec_num]) #